Abstract
Currently, the web-based Named-Entity relationship extraction has been a new research field with a tremendous potential. The goal of web-based entity relationship extraction is to explore the relationship between a set of realistic entities. It’s a challenging research field and has a widely application value in the related fields of text mining. In this paper, we propose a newly defined framework called Snowball++ based on the traditional entity relationship extraction frameworks. In our Snowball++ framework, we focus on the many-to-many relations more than one-to-one relations. The system is also implemented in the many-to-many manner and it improves the precision and recall. It’s worth to notice that Snowball++ will assign a specific relation type to each entity-relationship pair and the whole training process only need a few manual labor. For the sake of building a efficient and scalable system, we implement the Snowball++ framework on the Hadoop platform which is a totally distributed computing system. Eventually, the experiments show that our framework and implementation are efficient and effective.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (Grant NO. 61300137), the Guangdong Natural Science Foundation, China (NO. S2013010013836), Science and Technology Planning Project of Guangdong Province China NO. 2013B010406004 the Fundamental Research Funds for the Central Universities, SCUT(NO. 2014ZZ0035).
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Li, J., Cai, Y., Wang, Q., Hu, S., Wang, T., Min, H. (2015). Entity Relation Mining in Large-Scale Data. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_10
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DOI: https://doi.org/10.1007/978-3-319-22324-7_10
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